Detection of Outliers in LiDAR Data Acquired by Multiple Platforms over Sorghum and Maize

High-resolution point cloud data acquired with a laser scanner from any platform contain random noise and outliers. Therefore, outlier detection in LiDAR data is often necessary prior to analysis. Applications in agriculture are particularly challenging, as there is typically no prior knowledge of t...

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Autores principales:	Behrokh Nazeri, Melba Crawford
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	outlier removal remote sensing LiDAR leaf area index deep learning Science Q
Acceso en línea:	https://doaj.org/article/f7f77a6e54344f13aa1b7b9960399d70
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spelling	oai:doaj.org-article:f7f77a6e54344f13aa1b7b9960399d702021-11-11T18:56:58ZDetection of Outliers in LiDAR Data Acquired by Multiple Platforms over Sorghum and Maize10.3390/rs132144452072-4292https://doaj.org/article/f7f77a6e54344f13aa1b7b9960399d702021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4445https://doaj.org/toc/2072-4292High-resolution point cloud data acquired with a laser scanner from any platform contain random noise and outliers. Therefore, outlier detection in LiDAR data is often necessary prior to analysis. Applications in agriculture are particularly challenging, as there is typically no prior knowledge of the statistical distribution of points, plant complexity, and local point densities, which are crop-dependent. The goals of this study were first to investigate approaches to minimize the impact of outliers on LiDAR acquired over agricultural row crops, and specifically for sorghum and maize breeding experiments, by an unmanned aerial vehicle (UAV) and a wheel-based ground platform; second, to evaluate the impact of existing outliers in the datasets on leaf area index (LAI) prediction using LiDAR data. Two methods were investigated to detect and remove the outliers from the plant datasets. The first was based on surface fitting to noisy point cloud data via normal and curvature estimation in a local neighborhood. The second utilized the PointCleanNet deep learning framework. Both methods were applied to individual plants and field-based datasets. To evaluate the method, an F-score was calculated for synthetic data in the controlled conditions, and LAI, the variable being predicted, was computed both before and after outlier removal for both scenarios. Results indicate that the deep learning method for outlier detection is more robust than the geometric approach to changes in point densities, level of noise, and shapes. The prediction of LAI was also improved for the wheel-based vehicle data based on the coefficient of determination (R<sup>2</sup>) and the root mean squared error (RMSE) of the residuals before and after the removal of outliers.Behrokh NazeriMelba CrawfordMDPI AGarticleoutlier removalremote sensingLiDARleaf area indexdeep learningScienceQENRemote Sensing, Vol 13, Iss 4445, p 4445 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	outlier removal remote sensing LiDAR leaf area index deep learning Science Q
spellingShingle	outlier removal remote sensing LiDAR leaf area index deep learning Science Q Behrokh Nazeri Melba Crawford Detection of Outliers in LiDAR Data Acquired by Multiple Platforms over Sorghum and Maize
description	High-resolution point cloud data acquired with a laser scanner from any platform contain random noise and outliers. Therefore, outlier detection in LiDAR data is often necessary prior to analysis. Applications in agriculture are particularly challenging, as there is typically no prior knowledge of the statistical distribution of points, plant complexity, and local point densities, which are crop-dependent. The goals of this study were first to investigate approaches to minimize the impact of outliers on LiDAR acquired over agricultural row crops, and specifically for sorghum and maize breeding experiments, by an unmanned aerial vehicle (UAV) and a wheel-based ground platform; second, to evaluate the impact of existing outliers in the datasets on leaf area index (LAI) prediction using LiDAR data. Two methods were investigated to detect and remove the outliers from the plant datasets. The first was based on surface fitting to noisy point cloud data via normal and curvature estimation in a local neighborhood. The second utilized the PointCleanNet deep learning framework. Both methods were applied to individual plants and field-based datasets. To evaluate the method, an F-score was calculated for synthetic data in the controlled conditions, and LAI, the variable being predicted, was computed both before and after outlier removal for both scenarios. Results indicate that the deep learning method for outlier detection is more robust than the geometric approach to changes in point densities, level of noise, and shapes. The prediction of LAI was also improved for the wheel-based vehicle data based on the coefficient of determination (R<sup>2</sup>) and the root mean squared error (RMSE) of the residuals before and after the removal of outliers.
format	article
author	Behrokh Nazeri Melba Crawford
author_facet	Behrokh Nazeri Melba Crawford
author_sort	Behrokh Nazeri
title	Detection of Outliers in LiDAR Data Acquired by Multiple Platforms over Sorghum and Maize
title_short	Detection of Outliers in LiDAR Data Acquired by Multiple Platforms over Sorghum and Maize
title_full	Detection of Outliers in LiDAR Data Acquired by Multiple Platforms over Sorghum and Maize
title_fullStr	Detection of Outliers in LiDAR Data Acquired by Multiple Platforms over Sorghum and Maize
title_full_unstemmed	Detection of Outliers in LiDAR Data Acquired by Multiple Platforms over Sorghum and Maize
title_sort	detection of outliers in lidar data acquired by multiple platforms over sorghum and maize
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/f7f77a6e54344f13aa1b7b9960399d70
work_keys_str_mv	AT behrokhnazeri detectionofoutliersinlidardataacquiredbymultipleplatformsoversorghumandmaize AT melbacrawford detectionofoutliersinlidardataacquiredbymultipleplatformsoversorghumandmaize
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Detection of Outliers in LiDAR Data Acquired by Multiple Platforms over Sorghum and Maize

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